MatchbookAI XDM

AWS Marketplace Overview
Matchbook XDM   is a cloud-native External Data Mastering (XDM) platform that extends traditional Master Data Management by combining internal enterprise records with trusted commercial, regulatory, and regional datasets at scale.

Built for the modern data stack, Matchbook XDM runs natively on AWS and enables organizations to rapidly cleanse, match, enrich, and govern B2B entity data across systems. Unlike legacy MDM platforms that require long deployments and complex rule management, XDM is API-first, SaaS-delivered, and low-code configurable, allowing teams to deploy in days and scale instantly.

XDM leverages Matchbook's proven AI-based entity resolution (core + anchor multi-pass matching) to accurately unify fragmented records from CRMs, ERPs, data lakes, and third-party data providers. The platform then enriches and governs those records to create context-aware golden entities optimized for Sales, Risk, Compliance, Operations, and Analytics.

Organizations can continuously publish mastered data to AWS data lakes, analytics platforms, and operational systems via streaming or batch pipelines while monitoring data quality, lineage, and stewardship activity in real time.

Key Benefits
Rapid deployment
Guided onboarding with initial loads in days
Typical rollout in under 30 days
Lower total cost
Up to ~80% lower TCO compared to custom or legacy MDM through automation and SaaS operations
Accurate AI matching
Explainable entity resolution with configurable rules and evidence panels
Trusted enrichment
Governed connectors to commercial, regulatory, and regional datasets
Business-ready golden records
Context-aware data models for Sales, Risk, Compliance, Tax, and Analytics use cases
Always-on quality monitoring
Streaming validation, anomaly scoring, dashboards, and alerts
Self-service stewardship
Low-code policies with full audit trails and rollback capabilities
Elastic AWS scale
Consumption-based processing aligned to AWS infrastructure
Core Capabilities
Entity Resolution & Matching
Unify fragmented records across CRMs, ERPs, data lakes, and external datasets using configurable AI-driven matching and auto-promotion for high-confidence results.
Entity Enrichment
Combine multiple external and internal data sources using ordered source hierarchies with provenance and reliability scoring.
Cleansing & Locale Normalization
Standardize global business data, including addresses, postal codes, transliteration, and business suffix normalization.
Confidence Scoring & Auto-Accept Policies
Composite confidence scoring across match strength, source reliability, geography, and data freshness to automate approvals or route to stewardship.
Hierarchy & Relationship Graphs
Build parent/child hierarchies and supplier-customer networks for complete business entity visibility.
Stewardship Workbench
Exception queues, side-by-side comparisons, merge/split workflows, and full audit history.

Common Use Cases
-Customer 360 and lead enrichment
-Global supplier and vendor mastering
-Data lake cleansing and governance before analytics or AI
-Compliance, AML, and KYC augmentation
-M&A data harmonization and post-merger integration
-Spend analytics and supplier diversity reporting
-Who Uses Matchbook

Leading enterprises across technology, healthcare, industrial, and financial services rely on Matchbook platforms to power trusted analytics, revenue operations, supplier intelligence, and regulatory reporting.
Contact: sales@matchbookai.com  Website